sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6204976b38e05a6f6bb801808a20447edc0cbcafab50c0c95b586d267d73642c | R | 14,860 | 365 | ---
title: "WBC Walkthrough"
author: "Jack Barrington"
date: "`r format(Sys.time(), '%a %d %B %Y')`"
output:
html_document:
toc: true
toc_depth: 2
theme: readable
toc_float: true
knit: (function(input, ...) {
rmarkdown::render(
input,
output_dir = here::here("Output"),
output_fil... |
5992dee75bb2fa44dbebdcfed242cf678c1439708da8bd1f920e1e5cec69e14d | R | 14,879 | 418 | # this script will preprocess the aggregated multiomes
# to run locally:
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/cellranger_multi_counts:$HOME/cellranger_multi_counts \
# -v /mnt/g/ckd:$HOME/ckd \
# -v $HOME:$HO... |
d2a6d3d00e70669bd8d6ffb0ce8848f335ec169f30765e404875683da315f1cd | R | 14,900 | 489 | source("paper.R")
# * configs
projdir <- here::here()
outdir <- file.path(projdir,
"supple.datashare",
"supplementary.tables/October")
fprefix <- "SupplementaryTable"
# * meta
atacMeta <- readRDS(cembav2env$sa2metaFile)
# * Supplementary 3
# See heavySummaryOfTransferLabel.R in for details
# * Supplementary 4
# ... |
cd0dbb3ce81d3ca67a323bc46a7f1dbb1220795d44230a574ee4bce7cebaf9e5 | R | 14,902 | 313 | #' Exact tests to detect mutually exclusive, co-occuring and altered genesets.
#'
#' @description Performs Pair-wise Fisher's Exact test to detect mutually exclusive or co-occuring events.
#' @details This function and plotting is inspired from genetic interaction analysis performed in the published study combining gen... |
1fad3b9a9a74339b817852da8c385e45d62d416efe37ab61579d8279b76c3658 | R | 14,938 | 379 | #run lmer on diagnosis with and without controlling for quality, and with thresholding
library(plyr)
library(ggplot2)
library(ggseg)
library(ggsegGlasser)
library(dplyr)
library(colorspace)
library(effectsize)
library(lmerTest)
library(ggpubr)
#Analyses in this script:
# 1. Dx effect only
# 2. Dx effect controlling ... |
038fa7fad25b0ae6d188074c5289a25d09c8a7da832f4fb1cb24f2dbfe50c728 | R | 14,962 | 309 | ### LC snRNA-seq analysis
### Mito rate QC & doublet assessment
### Initiated: MNT 20Dec2021
library(SingleCellExperiment)
library(DropletUtils)
library(scater)
library(scran)
library(rtracklayer)
library(BiocParallel)
library(jaffelab)
library(gridExtra)
library(here)
library(sessioninfo)
here()
#[1] "/dcs04/lie... |
0ab2a2a2b208cdd1b80245f704834cfb7ff1bb7a60045372c3d9a7d3ca683c5e | R | 14,979 | 355 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
library(ComplexHeatmap)
library(circlize)
library(wesanderson)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(string... |
2dab7347711137d86c35d43eb9a647ffbe02d6c3abe6df80cf6e594732333ce0 | R | 14,999 | 331 | # Gregory Way 2017
# PanCancer Classifier
# scripts/viz/ras_benchmarking_figures.R
#
# Compares performance of Ras classifier in 3 different scenarios
#
# 1) Mutation vs. Copy Number vs. Both
# 2) Dropping Ras vs. Dropping Rasopathy vs. no Drop
# 3) Expression vs. Covariates vs. Both
#
# Also compares classifier coeffi... |
def85e2ef53687da23cef5a9c4f68353ff2735ef5589f5faabfb5db200c93162 | R | 15,042 | 386 | # predict excitatory neuron projecting
load("../scRNA/GSE161936_Lui_Nguyen_Seuratobjects.RData")
integrated_Exc = subset(merFISH_integrated, CellType == "Ext")
DefaultAssay(Fig3Rbp4retro) <- "RNA"
Fig3Rbp4retro@assays$integrated = NULL
#
integrated_Exc_sample = subset(integrated_Exc, slice %in% c("44"))
SpatialPlot... |
9cdb202fb79aef2502a050d6bea76064fae5f4cf6bdf5733e5ca44b6951483d9 | R | 15,096 | 258 | ---
title: "Model Evaluation for 410 N2a TRAP Data, with Updated DNA Counts"
author: "Stephen P Plassmeyer"
date: "Last Updated, November 18, 2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
library(nortest)
```
# Overview... |
b6c9f0f4dbe9d66cca66b4d354fa002cfa96c58aa022b7f6b7e0c3cdedb1fa87 | R | 15,120 | 376 | # call LOY barcodes for the atac samples using finite mixture models and write an annotated seurat obj
# RUN local interactive
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/g/reference:$HOME/reference \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/cell... |
e124eab47b97463d368f3b2357dce22580ad6f23be3dc964b64f712e8fa409fa | R | 15,351 | 347 | #############################################################
# Generate Figure 3, Supplementary Figure 4,6,7
#############################################################
library(ggplot2)
library(scales)
library(ggpubr)
library(clusterProfiler)
library(openxlsx)
library(org.Hs.eg.db)
library(org.Mm.eg.db)
... |
18b838777f714d766d3a7555485161d55461e051084dfb14857f2de5bb791789 | R | 15,396 | 356 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
library(ggpubr)
## AUCell object for DNA damage estimates
library(AUCell)
## statistical analyses
library(tidymodels)
library(lme4)
library(broom.mixed)
library(lmerTest)
ss <- func... |
f13f090c0dcd7e9f4b79f6dbb51e8d6c9c4dc8a1b3c725b00155b2a1d61c69fb | R | 15,423 | 288 | library(SeuratDisk); library(Seurat); library(dplyr); library(ggplot2); library(patchwork);library(RColorBrewer)
library(ktplots); library(pheatmap); library(viridis)
#################
### GSE155698 ###
#################
dir()
# [1] "AdjNorm_TISSUE_1" "AdjNorm_TISSUE_2" "AdjNorm_TISSUE_3" "Healthy_PBMC_1" "He... |
27eb0b6cf7ce038d6239122642de9411583e39a7b361098240e713fd1090aac3 | R | 15,440 | 349 | #############################################################
# Generate Figure 3, 3E, 5F, Supplementary Figure 4,6,7
#############################################################
library(ggplot2)
library(scales)
library(ggpubr)
library(clusterProfiler)
library(openxlsx)
library(org.Hs.eg.db)
library(org.Mm.... |
ebe004609e09468256de2898684aab087c91067f6d67eb64f5c2c2e3ef3efa1e | R | 15,476 | 397 | ---
title: "HiC-Compartment_analysis_Mastropasqua_et_al_22"
author: "F Mastropasqua"
date: "10/4/2022"
output: html_document
---
```{r}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(dplyr)
library(ggplot2)
library(BiocManager)
```
##Generate composite eigenvector for somatic chromosomes
Load bedgraph files o... |
baaf20098ad9684fc14104b9c2b480d71b733d4008f9f8cce33505d56e24b4d2 | R | 15,526 | 407 | print_mat = function(maf, genes, removeNonMutated = TRUE, colors = NULL,
bgCol = '#ecf0f1', borderCol = 'white', fontSize = 1,
plot2 = FALSE, test = FALSE, clinicalFeatures = NULL, sampleOrder = NULL,
additionalFeature = NULL, additionalFeaturePch = 20, add... |
55ded72d16204f98b801d31582ad892c9f8d7522577df818c3395131211a2361 | R | 15,696 | 400 | #########################
# figure multiome umap
library(Seurat)
library(here)
library(ggplot2)
# create a plot dir
plots <- here("ckd","figures")
dir.create(here(plots))
multi <- readRDS(here("ckd","multi_aggr_prep","step3_multi_loy.rds"))
plotA <- DimPlot(multi, label=T)
plotA <- plotA +
theme_bw() +
theme(pl... |
e9e10b7e2e01cd3b900b2d5d30d9cc7de921281dd91d4522b4017a2f915c7ce5 | R | 15,753 | 301 | #' Plot the comorbidity analysis results in a network
#'
#'Given an object of class \code{cAnalysis} or \code{molecularcAnalysis} obtained from a comorbidity analysis,
#' a network is obtained.
#'
#' @param input A \code{cAnalysis} or \code{molecularcAnalysis} object, obtained
#' by applying the \code{comorbidityAnal... |
8b0154aa7d39a333cc70580d3996e60c23aafdd5e22cae2f338f9ac4ea5edddf | R | 15,823 | 433 | #!/usr/bin/env Rscript
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(gridExtra)
#set all directory paths
outdir <- paste0("/gladstone/bioinformatics/adnetworksppg/Project_1_Huang/NK01/",
"results/HMGB1_snRNA_mm10/06_seurat_analysis_batch1_batch2_merged")
indir <- pas... |
16986e727d35cdea900c18282c6affb9d6487845784ce49e3b6f3cbc6f373b0e | R | 15,853 | 414 | #This script has the following goals:
#1-Get the final sample for DWI preprocessing, fixel reconstruction and NMF
#2-Create the list of subjects for FOD template creation
#3-Create the specific NMF data input file structure (list of bblids). This will be possible with using fixel_to_h5.sh as NMF will easily read h5 fil... |
2d3cc9d8cc7c0f091f339072cc4c295c51a86525b859eb1ef84b3d3cf69c9628 | R | 15,873 | 304 | # MLAging project DY@WebbLabBrown
# data: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE188646
# from paper: https://doi.org/10.1038/s43587-022-00246-4
library(tidyverse)
library(Seurat)
##--1.read data from 10x and perform preprocessing----
Aged_1 <- Read10X(data.dir = '../filtered_feature_bc_matrix/Aged_1/f... |
a3693d29c4b45086e8edcd3139c8132fee5829f54e9bd5d1970191dfdff2a61e | R | 16,085 | 325 | library(tidyverse)
library(ggplot2)
library(ggpubr)
library(ggpattern)
library(patchwork)
library(lsa)
library(rstatix)
setEPS()
process_embeddings <- function(embbedings,dataInfo,sampleInfo){
dataInfo <- dataInfo %>% select(sig_id,cmap_name,duplIdentifier) %>% unique()
sampleInfo <- left_join(sampleInfo,dataIn... |
60a4f8f58c2d94484919d2c23b1b9877d25bb4aa9edc5b6897e95b92cacbf612 | R | 16,113 | 304 | library(GenomicFeatures)
library(DESeq2)
library(Rsamtools) #needed for BamFileList
library(rtracklayer) #needed to import GTF as GRanges object
library(GenomicAlignments) #used for summarizeOverlaps
library( "RColorBrewer" ) #for making heatmaps
library(gridExtra)
library("pheatmap") #used for heatmaps
library(dplyr)
... |
aab979f7fe2375eb785b576d19304f86ac5461b3e6cd09a3c1538e85e1741126 | R | 16,141 | 291 | library(Seurat)
library(BiocGenerics)
library(S4Vectors)
library(plotly)
library(htmlwidgets)
library("rmarkdown")
library(Seurat)
library(monocle3)
library(scales)
library(igraph)
library(ggraph)
library(graphlayouts)
library(qgraph)
library(AUCell)
library(ComplexHeatmap)
library(circlize)
library(RColorBrewer)
setw... |
e28e4f105e9e311e0cb6ad222b9a335906b12e3a990c1f8a0bfad8389b69f4fa | R | 16,151 | 332 | ## SOFTWARE COPYRIGHT NOTICE AGREEMENT
## Copyright (c) 2003-2018 Regents of the University of California and Broad Institute. All rights reserved.
##
## This software is supplied without any warranty or guaranteed support
## whatsoever. Neither the Broad Institute nor MIT can be responsible for its
## use, misuse, or ... |
acba913666ccd0afc14cb7a7859238df3d1860466f57530f1c525f88053525ca | R | 16,153 | 378 |
#####cell lysates from my BV2 polarization experiment Data Analysis - Aditya Natu - Rangaraju Lab
###Data: mass spec data on the cell lysates from my BV2 polarization experiment (with gene names on the far right column). From this I would like to see if the treatments I added (LPS, TGFB, IL-10) did in fact polarize m... |
f8b62e81c62653197ff679a4f0733a319f3fd278c4baa7255dde792ffa705f88 | R | 16,165 | 475 | source("paper.R")
# * configs
outdir <- file.path(projdir, "figures", "data/fig4")
if (!dir.exists(outdir)) {
dir.create(outdir)
}
TF.df.co <- data.table::fread(
file = file.path(projdir, "22.sa2GRN", "src/main/resource",
"CisBP_ver2_Mus_musculus.motif2factors.txt"),
header = TRUE,
data.table = FALSE
)
... |
0071c80c1e245bc1664309518875940bd0d363d373a1dd7c43513ca80fff0a99 | R | 16,184 | 275 | ---
title: "Model Evaluation for 410 N2a TRAP Data, with Updated DNA Counts"
author: "Stephen P Plassmeyer"
date: "Last Updated, 07-11-2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
library(nortest)
```
# O... |
186c93f05f54228edf2ec0e5a72f585e4428f96e1347ef37dd80fe97bafb4140 | R | 16,186 | 420 | #' The color palettes from SnapATAC
#' @export
SnapATACPalette <- c("grey", "#E31A1C", "#FFD700", "#771122",
"#777711", "#1F78B4", "#68228B", "#AAAA44",
"#60CC52", "#771155", "#DDDD77", "#774411",
"#AA7744", "#AA4455", "#117744", "#000080",
"#44AA77", "#AA4488", "#DDAA77", "#D9D9D9",
"#BC80BD", "#FFED6F", "#7... |
6284cf36e8f6ed4a965c1ae5a12ecc66f2156484d93e35a6fc44415cdbb46373 | R | 16,303 | 440 | #thresholding analysis - EULER cut offs
library(lmerTest)
library(ggpubr)
library(effectsize)
library(parameters)
library(plyr)
library(ggplot2)
library(ggseg)
library(ggsegGlasser)
library(dplyr)
library(colorspace)
#lmer including partial R
#cut offs by median absolute deviation
#find and define cut off points u... |
de3ede546a88d0f94a4571534acea15f91e4d8b755bc8ffa621e04cfeb18718d | R | 16,312 | 381 | # some plots
setwd("/nfs4/chaozhang/proj/Neuron/Aritra/merFISH/script")
source("functions.R")
L3_color = c( pals::parula(7), as.vector(pals::plasma(6))[1:5],
"#AA0DFE","#993F00","#F99379","#604E97", "maroon2", "yellow3")
L3_color = as.character(pals::polychrome())[3:30]
# plot cell location
plot_cell_l... |
9f667684330c7c73ad813c8d83eab216c65acf5f909ef81e97d032832a74a358 | R | 16,316 | 359 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
library(ggpubr)
## AUCell object for DNA damage estimates
library(AUCell)
## statistical analyses
library(tidymodels)
library(lme4)
library(broom.mixed)
library(lmerTest)
ss <- func... |
ef9ac8b67edcf116a8b0b26041cc272746ddd81ab3924f8398886c80801d7c6e | R | 16,342 | 412 | ###################
##### Figures #####
###################
# Last edited: 17/06/23 by LVB
# Description: Manuscript and SI figures and tables.
#----- Packages
library("tidyverse")
library("ggbeeswarm")
library("kimura")
library("reshape2")
#----- Code
source("00-plot-setup.R")
#----- Data
load("data/parsed/01-pa... |
5fef835520131a30f1b4b6ebe3592ee0ef6755be8ef3eb0b90bd275409610495 | R | 16,364 | 474 | ---
title: "Spatial Transcriptomics Deconvolution with `SPOTlight`"
date: "`r BiocStyle::doc_date()`"
author:
- name: Marc Elosua-Bayes
affiliation:
- &CNAG-CRG National Center for Genomic Analysis - Center for Genomic Regulation
- &UPF University Pompeu Fabra
email: marc.elosua@cnag.crg.eu
- name: Helena L. C... |
39949412e68cfc31ae745245e2904866df078d0d3a318a56ee8363819faf77b7 | R | 16,401 | 357 | # 48 - Data Prep
#update on 5.5.21, made following modifications
## add severity covariate
## add der_her and der_cdxx
## der_txline will be derived later
## add covid therapy
## update on 7.1.21: add der_o2_ever <supplemental oxygen>
## update on 8.26.21: extract der_cdk46i, der_her2 from 8.25.21 raw data
## update on... |
9b1799e45df54845bf2482d505a1888265c030c0eacc7fe9ca4904e5cd2127ac | R | 16,455 | 392 | ---
title: "N2a Replicate Correlation Analysis"
author: "Stephen P Plassmeyer"
date: "8/6/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
library(ggpubr)
```
# Load and Format Data
```{r}
compiled_counts <- fread('../../.... |
6d27d7daec3a865f5efe73b8823225514ca5258c730e197fe6e10ceae0e26fa3 | R | 16,483 | 219 |
### ----------------------------------- ###
### Data Cleaning - Full phenotype tables
### Neville Magielse, 2022
### ----------------------------------- ###
# We are using R version 4.1.0 (2021-05-18)
# Nota bene: we specified dplyr functions specifically in our code i.e., dplyr::select. Its functions otherwi... |
867521db6f33947a3cd75fab313aaa1e41616dd02a32c3e185002e296552767e | R | 16,494 | 367 | ---
title: "Figure 4"
author: "Joe Raymond"
date: "August 10, 2021"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(tidy = TRUE)
knitr::opts_chunk$set(eval = TRUE)
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_chunk$set(fig.height = 4)
knitr::opts_chunk$... |
c1ba7f0ee650ef4b2e6e83d987a1c100cfea008a39e14dd79a5a299b5744fa0b | R | 16,602 | 361 | #' Build cluster-wise list of gene expression statistics from scRNAseq data
#'
#' This function takes a \code{Seurat} or \code{SingleCellExperiment} object and
#' builds a list of dataframes containing gene expression statistics for all
#' genes of each cluster. This can be used as the input to
#' \code{\link{BuildCCIn... |
cea7421304488c626a1bff2dc157040039ad2ece2f11c89a838cfc6ba978db17 | R | 16,605 | 432 |
#QC:
#This script executes a standardised QC procedure on cellranger-processed single cell 10x data. It does the following
# Filter cells and genes, based on Qc metrics such as mitochondrial ratio, number of genes, total UMI, confident read alignment rate (i.e. proportion of reads from the cell that were confidently a... |
407cae3b17e48d64f3659bc3635f5b330db81b9287b65f1b71e9627cc23ea1aa | R | 16,640 | 578 | ##################################################################################################
# LC snRNA-seq analysis alternative workflows: unsupervised clustering and supervised thresholding
# Lukas Weber, July 2022
#################################################################################################... |
89fb06092fdb8e6b34b4eb2c0fb9aeae59d125ef267cbc207edfc08864a4e2ae | R | 16,651 | 381 | # Fit the published bilateral model, compare TBV adjustment, and recreate figures.
args <- commandArgs(FALSE)
file_arg <- args[grepl("^--file=", args)]
this_file <- if (length(file_arg) > 0) sub("^--file=", "", file_arg[1]) else "scripts/03_fit_bilateral_model.R"
this_file <- gsub("~+~", " ", this_file, fixed = TRUE)
s... |
16d45d7aa45fc92d430fd016e3c9e815db06221c0d2f2699255e91fe560705cf | R | 16,664 | 456 | #run locally on Ayushi's laptop
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(scales)
#set all directory paths
basedir <- paste0("~/Dropbox (Gladstone)/YH_MN02/",
"seurat_analysis_no_S521G/")
outdir <- paste0("~/Dropbox (Gladstone)/YH_MN02/",
"seura... |
adc9a49b2eb4aa465f0006e578e2e03cf1fcf855d31590ce5fc9c27bb98b7022 | R | 16,712 | 296 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
3f37ac7804fdaad3748c57eca5b8041e30643ba665c7896a05f9843e02ff3122 | R | 16,732 | 578 | ---
title: "LC analyses: preprocessing summaries"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 2
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
# Introduction
C... |
92104246d9ad4cd921786a71cac6309d02a86c6b15a248978539eda8f10f1a97 | R | 16,766 | 507 | #' @rdname geom_text_repel
#' @export
geom_marquee_repel <- function(
mapping = NULL, data = NULL, stat = "identity", position = "identity",
...,
box.padding = 0.25, point.padding = 1e-6,
min.segment.length = 0.5,
arrow = NULL,
force = 1,
force_pull = 1,
max.time = 0.5,
max.iter = 10... |
6174a56b49374359a427b188960e06a76dfa76a8b8ad92bfa3f7b3a2e1dd616b | R | 16,798 | 495 | ---
title: "MES Timecourse JS"
author: "Jared Slosberg"
date: "5/5/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r init, results = 'hide', message = F}
#source('scripts/init.R')
library(BUSpaRse)
library(monocle3)
library(DropletUtils)
library(ggplot2)
library(... |
362e5f589112935a451769582b65cedb3fcb7037cc0f994c56e3ae39042467bf | R | 16,800 | 296 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
9e8e43e86f7ae979e8cd26504786b58616c3d77a267cd3dac76750659c19a66c | R | 16,853 | 437 | ---
title: "RNAseq_DEG_analysis"
author: "F Mastropasqua"
date: "10/4/2022"
output: html_document
---
```{r}
```
```{r}
knitr::opts_chunk$set(error = TRUE)
#load libraries
library(DESeq2)
library(ggplot2)
library("biomaRt")
library(dplyr)
library(tibble)
```
## Transcriptome analysis at three timepoints
The bulk R... |
ed6bdec6d99d447c14e3d5e8377ffc9c4bb3f2a1f09612d1bf6f00c4cc4604b9 | R | 16,883 | 424 | #!/usr/bin/env Rscript
# this script will preprocess aggregated snATACseq data
# counted and aggregated by cellranger-atac v2.1 without library normalization
#
# to run locally
# counts=/mnt/g/cellranger_atac_counts
# project=/mnt/g/ckd
# SCRATCH1=/mnt/g/scratch
# docker run -it \
# --memory 100g \
# --cpus 4 \
# --wor... |
923663e53855bd18a92e0508c161a3215bf089673ed7033b0c16f8b69c2dd089 | R | 16,901 | 461 | #!/usr/bin/env Rscript
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(gridExtra)
#increase RAM usage so the futurized Seurat functions...
#can access larger global variables
options(future.globals.maxSize = 4000 * 1024^2)
#set all directory paths
outdir <- paste0("/gladstone/bioinfo... |
c03c648719955fa53543d677ef164e326395bbe8a6ef4f524de1f50a7f4c5743 | R | 16,936 | 250 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/hg38/hic/config.R'))
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,... |
a41571a509bc61cc7418f9091dfc918305f07470f56545e246ea54f9a889172d | R | 17,019 | 308 | '''
# Citation: Under Revision
# Email: qususu@cibr.ac.cn
# Date: 2023-05-11
# Firstly, fastq generation, demultiplexing and alignment were processed by the Basecall software and CellRanger v6.0.1.
# Next, all data was processed via Seurat v4.0.3 in R v4.1.0 according to the following pipeline.
# This file conta... |
560726ca81bebb3e08b760a62de902385e16e8a08ccd6013240acfab2b97581a | R | 17,071 | 451 | #!/usr/bin/env Rscript
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(gridExtra)
#set all directory paths
outdir <- paste0("/gladstone/bioinformatics/adnetworksppg/Project_1_Huang/NK01/",
"results/HMGB1_snRNA_mm10/06_seurat_analysis_batch1_batch2_merged")
indir <- pas... |
0150b0cb8259d81ee49fcfbc09626bfea22d5ba79ac7f8c75d40e657332d8bb9 | R | 17,087 | 518 | #####################################
# LC Visium analyses: quality control
# Lukas Weber, Oct 2022
#####################################
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(here)
library(SpatialExperiment)
library(scater)
libr... |
ed6c413bdb204d65dd3177639315a4c08881a4fc980e04afa361976bdf8e91a6 | R | 17,231 | 306 | ---
title: "Developing mpralm Style Realistic Simulation"
author: "Stephen P Plassmeyer"
date: "4/3/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../code/mpra_tools/mpra_tools.r')
library(progress)
```
```{r}
compiled_counts <- fread('../../data/compiled_cou... |
4e024689209dce81f49f3e5d38ea0f663662f1fbf5ac1c1c29b47283bf654cf5 | R | 17,263 | 325 | library(tidyverse)
library(lsa)
library(ggplot2)
library(ggpubr)
library(ggridges)
library(doFuture)
# parallel: set number of workers
cores <- 11
registerDoFuture()
plan(multisession,workers = cores)
library(doRNG)
#Process function to add condition ids and duplicate ids
process_embeddings <- function(embbedings,dataI... |
e3db77c09d0e442f058e1ca3dd918277df53cd79b81d56cb1a5c21421b6057f9 | R | 17,278 | 755 | ---
title: "LC analyses: segmented analyses"
author: "Lukas Weber"
date: "19 Nov 2020"
output:
html_document:
toc: true
number_sections: true
toc_depth: 2
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
Analyses for LC project... |
30799050ccf6ce95c88a90fce9eb3ae9864ca770497639082d4ac8866c4b13da | R | 17,311 | 405 | ---
title: "N2a Replicate Correlation Analysis"
author: "Stephen P Plassmeyer"
date: "11/10/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
library(ggpubr)
```
# Overview
This is a re-run of the correlation analysis code... |
cbb7bc08ff0392f0298b42ce55f2678efe68f71b096984507e8fb8c59d9d13a1 | R | 17,340 | 424 | library(tidyverse)
library(cmapR)
library(doFuture)
# parallel set number of workers
cores <- 15
registerDoFuture()
plan(multisession,workers = cores)
########## The whole pre-processing analysis is in the L1000 folder of the new data ###############
# Check L1000 documentation for information.
geneInfo <- read.del... |
de0e566f3768f332d42375e8f0a8205ba00f0541a6eb97ba8596ed8bbfda300f | R | 17,360 | 465 | setwd(file.path(path.expand(dirname("~")), "Downloads"))
library(tidyverse)
library(lme4)
library(car)
library(zoo)
library(ggplot2)
library(qqplotr)
library(patchwork)
library(interactions)
library(effects)
library(MuMIn) # for effect size
library(sjPlot)
library(dplyr)
library(performance)
WRITE.PLOTS <- T
anova_f... |
f4e78d7585f5d8a0eba6235baee8041718bfee74d3f6f92109d5dfda550ffe0b | R | 17,404 | 367 | #KEGG Enrichment Analysis of the enriched genes
#this script uses clusterProfiler::enrichKEGG() function
#run locally on Ayushi's laptop
#load required packages
library(Seurat)
library(org.Mm.eg.db)
library(clusterProfiler)
library(tidyr)
library(dplyr)
library(ggplot2)
library(EnhancedVolcano)
library(scales)
librar... |
77f57d818bb3a01ef424e7d047547c7d362a616966e3ba60f02f0c7ec279969a | R | 17,479 | 408 | # library import
library(Seurat)
library(SeuratDisk)
library(dplyr)
library(Matrix)
library(ggplot2)
library(ggpubr)
library(clustree)
library(dendextend)
library(ComplexHeatmap)
# define functions
##### helper functions
# by Benjamin Doran, slightly adapted by LA
ChooseClusterResolutionDownsample <- f... |
1789b502c12d9f5a47347ae457b189122d97cb9cf41a15b81c20f8002dffeb5a | R | 17,487 | 350 | ---
title: "Cui2020Rep_SumWeightsBarPlots"
output: html_document
date: '2022-08-30'
---
# This is ASK's replication of Zaixu's script "Step_5th_SumWeights_EF.R" that
# will try to recapitulate Figure 7C in his 2020 Neuron paper showing the sum of
# the importance of topographic features by network.
# load in libra... |
5b743cb2f41949b9cad89f75ab1d60a863819d1d965d45c38cd644b2fb81fbfb | R | 17,518 | 327 | #### Set working dir and source some functions ####
setwd("/Users/derekvantilborg/Dropbox/PycharmProjects/MoleculeACE")
source('Experiments/figures/R_scripts/vis_utils.R')
#### Data prep ####
# Import data
benchmark = read_csv('/Users/derekvantilborg/Dropbox/PycharmProjects/MoleculeACE/MoleculeACE/Data/results/Molec... |
f3e4e57b2188186860cfe288ea922b06b4847056e2a1b452ddd519d49fcdd10a | R | 17,562 | 403 | ---
title: "Comparing Vglut Data Sets"
author: "Stephen P Plassmeyer"
date: "4/23/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../code/mpra_tools/mpra_tools.R')
```
### Load Data
```{r}
compiled_counts <- fread('../../data/compiled_counts/asd3utr_compiled_c... |
d1a16607a915ad8a515338df36a853eb55f816464c8b20026111119082229eae | R | 17,639 | 368 | #!/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
library(diffHic, quietly = TRUE, verbose = FALSE)
library(GenomicRanges, quietly = TRUE, verbose = FALSE)
require(edgeR)
require(csaw)
require(statmod)
cells=args[1]
cells=as.vector(unlist(strsplit(cells,',')))
chr=as.character(args[2])
binSize=as.numeric(args... |
68c2a036d81b71ab88acc187f9d77cca38df07c451826153a5d6dc46702e3936 | R | 17,666 | 418 | ### MAGMA results
### code by Matthew N Tran
### last updated by Lukas M Weber, Oct 2022
### MAGMA with locus coeruleus (LC) snRNA-seq NE neuron cluster
# - Compile gene set analyses (step3) results
# - Explore gene-level stats for provided summary stats
library(rtracklayer)
library(GenomicRanges)
library(ja... |
ec5047ca3f95b09cffed6656564105499e7611499dfcd4192986480ed6a6fa52 | R | 17,759 | 391 | #' Draws lollipop plot of amino acid changes on to Protein structure.
#'
#' @description Draws lollipop plot of amino acid changes. Protein domains are derived from PFAM database.
#' @details This function by default looks for fields 'HGVSp_Short', 'AAChange' or 'Protein_Change' in maf file. One can also manually speci... |
e3e6f3655ee4092b068ac0c9d5a603584a3df1ed32b251c8716fe080e7045296 | R | 17,815 | 345 | library(tidyverse)
library(reshape2)
library(gg3D)
library(ggsignif)
library(ggpubr)
library(caret)
### First evaluate performance in fibrosis---------
#human_embs <- data.table::fread('results/embs/LiverEmbs_human.csv') %>% select(-V1)
#mouse_embs <- data.table::fread('results/embs/LiverEmbs_mouse.csv') %>% select(-V... |
1399cfb5e5eb03215b791186704bcac4d984dd89dc760238ba90be60ce4bc133 | R | 18,135 | 473 | # to run locally:
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/cellranger_multi_counts:$HOME/cellranger_multi_counts \
# -v /mnt/g/kpmp:$HOME/kpmp \
# -v /mnt/g/diabneph:$HOME/diabneph \
# -v /mnt/g/reference:$HOME/re... |
0e98829a616b0e34c89691050438e82b1f562c0335497fd7887852baf033d628 | R | 18,180 | 719 | ---
params:
study: "StudyInfo"
converter_path: "converter_path"
title: "`r params$study`"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
theme: simplex
---
<style>
.navbar {
background-color:black;
}
.navbar-inverse .navbar-nav > li > a:hove... |
0392cead9e5f2b43b5775ad15e14640490886c45cd0d1736ddbccf48571b5fc4 | R | 18,181 | 652 | # This code is to
# 1. load the data and add more variables
# 2. calculate and plot the average ordered
# 3. calculate the percentage change of the area
# 4. linear mixed regression to time effects
# WANG. updated 09-Aug-2024
# WANG. 04-March-2024
# Remove all objects created before to prevent clash
rm(list = ls())
... |
81570b5f4fa01c793f76c4e3496c03999f7561fe520a63e56c08cb3cf8bdd639 | R | 18,209 | 373 | # # # Header # # #
#Code to generate Figures 3E and 6A in Davis et al, "Deep topographic proteomics of a human brain tumour"
#Author: Phil Charles
#Date: 15.02.22
# !! Main analysis starts on line 224, everything prior is setup !!
# # # Setup # # #
#Load libraries
library(data.table)
library(ANF)
library(Spectrum)... |
24298111dabaf4bda842eb60dc43560a5b03e059543243ee69da2e809fbd823a | R | 18,210 | 466 | buildReferenceFromSeurat <- function(
obj, assay = 'RNA', verbose = TRUE, save_umap = TRUE, save_uwot_path = NULL
) {
if(!assay %in% c('RNA', 'SCT')) {
stop('Only supported assays are RNA or SCT.')
}
res <- list()
## TODO: check that these objects are all correctly initialized
res$Z_corr... |
4937057ede63230c817d3572dda4e45dce587522cc7003cdb0d75d72d127cce6 | R | 18,265 | 416 | library(Seurat)
library(tidyverse)
library(Matrix)
library(scales)
library(cowplot)
library(RCurl)
library(dplyr)
library(ggplot2)
### load dataset
setwd("~/spider_genome/singlecell")
for (file in c("F3_filtered_feature_bc_matrix",
"F2_filtered_feature_bc_matrix",
"M1_filt... |
2bd6dc692a8cbee14fa98edcb306127ce17ff983a5098ae80415ccea61eaf085 | R | 18,449 | 462 | # add celltype chromosomal RNA and ATAC median correction
# this script will analyze an aggregated multiome and call barcodes for LOY by RNA and ATAC using two finite mixture models
# implemented in mclust
# it will save a new seurat obj with the LOY annotations
# it will also generate a separate file with per barcode ... |
0c73cec70b5d610102e84c850cc7c5ce539345db98ffb9f8d98fe1d68a08f445 | R | 18,602 | 369 | #!/bin/env Rscript
source('/home/hpc/bonev/projects/hic/sc/config.R')
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,'scripts/temp_functions.R'))
library(doParallel)
registerDoParallel(8)
### In... |
b8210553fd4eec980db3ab2acad771e5a653ddc2c7a651d8c973fdbd90422ac7 | R | 18,653 | 368 | #!/bin/env Rscript
source('/home/hpc/bonev/projects/hic/sc/config.R')
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,'scripts/temp_functions.R'))
library(doParallel)
registerDoParallel(8)
### In... |
d85b5767d6aebfccdb2834a862cd499ecdfdb20e8cc6d99bed24e3f9dabc29a1 | R | 18,696 | 662 | ---
title: "LC analysis pipeline: spot-level cell type deconvolution"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 2
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```... |
1221df85d9161a7c858c9cba42adcb6258b05bd114aee87d91971f517c6bcf95 | R | 18,702 | 434 | library(Seurat)
library(dplyr)
library(cowplot)
library(Matrix)
library(ggplot2)
library(ggpubr)
library(clustree)
#install.packages('BiocManager')
#BiocManager::install('SeuratDisk')
# define functions
loadscRNAdataset <- function(data_path, n_feature = 500, mito_cutoff = 0.1, project = "NA"){
if (!... |
1d7b2254f66527911f117ec09c4338a3d1fc1f6eaf89f8f50ab24a8951a66e07 | R | 18,768 | 368 | ---
title: "Figure 6 in Gut Microbiome Dysbiosis is Associated with Increased Mortality following Solid Organ Transplantation"
author: Johannes Björk
output: html_notebook
# ==========================================================================================
# By: Weersma Group, TransplantL... |
2690536b7eaa955f573b6080297f015297774c248e2f7db3d3719dcfe2389897 | R | 18,777 | 397 | library(formattable)
library(plyr)
library(tidyr)
library(dplyr)
library(Hmisc)
library(writexl)
#update from 4.20.21 version - change coding approach for any CV, PULM, GAST
#update from 4.20.21.v2 version - update the order of cancer_status, remote first
#update from 4.23.21-
#add covariates: severity_of_covid_19_v2, ... |
f2fe251b5431f4ea8c71c7aabfcc6ac3a9bd523cfabd88d94cf3c89eaf547e20 | R | 18,803 | 300 | library(tidyverse)
library(gg3D)
#Process function to add condition ids and duplicate ids
process_embeddings <- function(embbedings,dataInfo,sampleInfo){
dataInfo <- dataInfo %>% select(sig_id,cmap_name,duplIdentifier) %>% unique()
sampleInfo <- left_join(sampleInfo,dataInfo)
embbedings <- embbedings %>% ro... |
b93fa64790236f21956f1ae7d243c8a0df6b5a851b7bc9a5a15a59b23abcd7c9 | R | 18,831 | 580 | library(cluster)
library(mclust)
# Function to compute probability of LL from an Mclust object. Requires two Gaussian clusters
# Probabilities returned by calling emProbLL(clusters, clusters$data) should be very similar to
# LL class prob from clusters$z
emProbLL <- function(clusters, latency) {
if (clusters$par... |
99c182abb0efff316699cbf9a608fb5b72a2e3c1c73414616e68bfafe72acf30 | R | 18,910 | 405 | #' Summarize CBS segmentation results
#' @details
#' A handy function to summarize CBS segmentation results. Takes segmentation results generated by DNAcopy package \code{\link{segment}} and summarizes the CN for each cytoband, chromosomal arms, and whole chromosomes.
#'
#' @param seg segmentation results generated fro... |
a76d170c889514dbd603d743d38c0807388cdc06873a3f4f9cfa4415a52287c2 | R | 18,916 | 313 | #' Extract single 5' and 3' bases flanking the mutated site for de-novo signature analysis. Also estimates APOBEC enrichment scores.
#' @details Extracts immediate 5' and 3' bases flanking the mutated site and classifies them into 96 substitution classes.
#' Requires BSgenome data packages for sequence extraction.
#'
#... |
65e7b20256aba9fa2e89b098f677aa65dce49c168017bd52d187be9c43b36dba | R | 18,964 | 446 | args = commandArgs(trailingOnly=TRUE)
if (length(args) < 10) {
stop("Usage: generate_feature_grid <trackdb> <track> <source track> <score filter> <feature1 data> <feature2 data>
<output_data> <range> <resolution> <output_image> <domains>")
}
construct.grid = function(interv1,interv2,min_dist,max_dist){
return(ddply... |
dddea2ce446e481e8d7e40a9728ece764d983e89aa0059cbd5ece3cabffe3bed | R | 19,031 | 266 | #!/usr/bin/R
suppressPackageStartupMessages(library("stringr"))
suppressPackageStartupMessages(library("dplyr"))
suppressPackageStartupMessages(library("tidyverse"))
suppressPackageStartupMessages(library("ggplot2"))
suppressPackageStartupMessages(library("ggpubr"))
suppressPackageStartupMessages(library("ggrepel"))
s... |
2f27f4f855a9287fce5417635a699fb1de9a9e1df6478e9f76b92933463d0d93 | R | 19,110 | 376 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/hg38/hic/config.R'))
library(MPRAnalyze)
library(ggplot2)
library(ggpubr)
library(pals)
library(cowplot)
library(patchwork)
library(circlize)
library(Hmisc)
library(Matrix)
library(matrixSta... |
5af1a74cd75befa57a92c4a5bcc64f3121d6b00808596c61b0deae52483d9f09 | R | 19,126 | 552 | suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
## We use this script to estimate the effectiveness of ... |
59ba47396e86f807bdbfb22e4e36a69c806a1f48ee2a7915104c072dc2a31f3a | R | 19,201 | 444 | # Gregory Way 2017
# PanCancer Classifier
# scripts/viz/ras_summary_figures.R
#
# Visualize summary for Ras Classifier Scores
#
# Usage: Run in command line
#
# Rscript --vanilla scripts/viz/ras_summary_figures.R
#
# Output:
# Several figures to summarize Ras findings
library(dplyr)
library(pheatmap)
library(ggplo... |
24db0af8cfc50ebc5ba8a274b877684537869fd02e2d358e6e45502322b44a5a | R | 19,217 | 611 | source("paper.R")
# * configs
atacMeta <- readRDS(cembav2env$sa2metaFile)
peakBed <- Sa2PeakCalling$loadFinalPeakBed()
scnmMap <- cembav2env$load.sa2.subclass.names()
nds <- 5000
# get class_color, nt_color, subclass to class and nt
sc2class <- unique(
atacMeta[,
c("subclass_label_v3", "class_label_v3", "class_c... |
43c2ae86ad8373f6c7cf362a3168e41be7ca48f9f8fe0227193d6c34aae7fb10 | R | 19,251 | 371 | library(data.table)
library(kimisc)
args <- commandArgs(TRUE)
##### get params #####
filedir <- args[1]
if(grepl("\\/$", filedir)==F){
filedir <- paste0(filedir, '/')
}
curfile <- thisfile()
source(paste(dirname(curfile), '/ConfigParser.R', sep=""))
config <- ConfigParser(file=paste(dirname(curfile),'/app.config', s... |
e7e2de17c6067f3f225ba68be62feeb1d2532223efb1419a36cfe24441c7ae5f | R | 19,261 | 487 | ################################################################################
# Neural substrates of parallel devaluation-sensitive and
# devaluation-insensitive Pavlovian learning in humans
# behavioral results
# last modified on MAY 2023
############################... |
6798bcc3593e703a5e56cdb797886e520a6694041c4578da7035cd46ac70aa9c | R | 19,263 | 408 | ---
title: "Figure 3, Vgat"
author: "Stephen P. Plassmeyer"
date: "02/15/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../../../code/mpra_tools/mpra_tools.r')
library(ggpubr)
library(MASS)
theme_set(theme_bw(base_size = 8))
```
```{r}
get... |
ac1301a76fd9551b8165b3c990b3d493c01f1d4e69533dcc57b92a86fb6bdf75 | R | 19,297 | 475 | ## CNV burden across cell types and LOY for all modalities
#############################
# figure proportion loy by age group
# note that only a minority of kpmp male samples have an age in the meta data (n=11)
library(here)
library(openxlsx)
library(dplyr)
library(ggplot2)
clinical_meta <- openxlsx::read.xlsx(here("c... |
f715921a654a1ab72c2b49cebc3daf4b9d62a9012abd633a60a26376bab9d779 | R | 19,315 | 593 | #' @rdname geom_text_repel
#' @param label.padding Amount of padding around label, as unit or number.
#' Defaults to 0.25. (Default unit is lines, but other units can be specified
#' by passing \code{unit(x, "units")}).
#' @param label.r Radius of rounded corners, as unit or number. Defaults
#' to 0.15. (Default ... |
fc32d4d3f81bae914f0c7731fd48d0f8ae291b5ddb2cbd9f0a2589d0e0451a6d | R | 19,444 | 440 | # Reconstruct literature effect sizes, pool them, and examine prior sensitivity.
args <- commandArgs(FALSE)
file_arg <- args[grepl("^--file=", args)]
this_file <- if (length(file_arg) > 0) sub("^--file=", "", file_arg[1]) else "scripts/02_create_prior_table.R"
this_file <- gsub("~+~", " ", this_file, fixed = TRUE)
sour... |
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